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The Patient Health Questionnaire (PHQ-9), the Beck Depression Inventory (BDI-II) and the Generalised Anxiety Disorder Assessment (GAD-7) are widely used in the evaluation of interventions for depression and anxiety. The smallest reduction in depressive symptoms that matter to patients is known as the Minimum Clinically Important Difference (MCID). Little empirical study of the MCID for these scales exists. A prospective cohort of 400 patients in UK primary care were interviewed on four occasions, 2 weeks apart. At each time point, participants completed all three questionnaires and a 'global rating of change' scale (GRS). MCID estimation relied on estimated changes in symptoms according to reported improvement on the GRS scale, stratified by baseline severity on the Clinical Interview Schedule (CIS-R). For moderate baseline severity, those who reported improvement on the GRS had a reduction of 21% (95% confidence interval (CI) -26.7 to -14.9) on the PHQ-9; 23% (95% CI -27.8 to -18.0) on the BDI-II and 26.8% (95% CI -33.5 to -20.1) on the GAD-7. The corresponding threshold scores below which participants were more likely to report improvement were -1.7, -3.5 and -1.5 points on the PHQ-9, BDI-II and GAD-7, respectively. Patients with milder symptoms require much larger reductions as percentage of their baseline to endorse improvement. An MCID representing 20% reduction of scores in these scales, is a useful guide for patients with moderately severe symptoms. If treatment had the same effect on patients irrespective of baseline severity, those with low symptoms are unlikely to notice a benefit. Funding. National Institute for Health Research.

Key Points Low statistical power undermines the purpose of scientific research; it reduces the chance of detecting a true effect. Perhaps less intuitively, low power also reduces the likelihood that a statistically significant result reflects a true effect. Empirically, we estimate the median statistical power of studies in the neurosciences is between ∼8% and ∼31%. We discuss the consequences of such low statistical power, which include overestimates of effect size and low reproducibility of results. There are ethical dimensions to the problem of low power; unreliable research is inefficient and wasteful. Improving reproducibility in neuroscience is a key priority and requires attention to well-established, but often ignored, methodological principles. We discuss how problems associated with low power can be addressed by adopting current best-practice and make clear recommendations for how to achieve this. Low-powered studies lead to overestimates of effect size and low reproducibility of results. In this Analysis article, Munafò and colleagues show that the average statistical power of studies in the neurosciences is very low, discuss ethical implications of low-powered studies and provide recommendations to improve research practices. A study with low statistical power has a reduced chance of detecting a true effect, but it is less well appreciated that low power also reduces the likelihood that a statistically significant result reflects a true effect. Here, we show that the average statistical power of studies in the neurosciences is very low. The consequences of this include overestimates of effect size and low reproducibility of results. There are also ethical dimensions to this problem, as unreliable research is inefficient and wasteful. Improving reproducibility in neuroscience is a key priority and requires attention to well-established but often ignored methodological principles.

Processing social feedback optimistically may maintain positive self-beliefs and stable social relationships. Conversely, a lack of this optimistic bias in depression and social anxiety may perpetuate negative self-beliefs and maintain symptoms. Research investigating this mechanism is scarce, however, and the mechanisms by which depressed and socially anxious individuals respond to social evaluation may also differ. Using a range of computational approaches in two large datasets (mega-analysis of previous studies, n  = 450; pre-registered replication study, n  = 807), we investigated how depression (PHQ-9) and social anxiety (BFNE) symptoms related to social evaluation learning in a computerized task. Optimistic bias (better learning of positive relative to negative evaluations) was found to be negatively associated with depression and social anxiety. Structural equation models suggested this reflected a heightened sensitivity to negative social feedback in social anxiety, whereas in depression it co-existed with a blunted response to positive social feedback. Computational belief-based learning models further suggested that reduced optimism was driven by less positive trait-like self-beliefs in both depression and social anxiety, with some evidence for a general blunting in belief updating in depression. Recognizing such transdiagnostic similarities and differences in social evaluation learning across disorders may inform approaches to personalizing treatment.

Improving the reliability and efficiency of scientific research will increase the credibility of the published scientific literature and accelerate discovery. Here we argue for the adoption of measures to optimize key elements of the scientific process: methods, reporting and dissemination, reproducibility, evaluation and incentives. There is some evidence from both simulations and empirical studies supporting the likely effectiveness of these measures, but their broad adoption by researchers, institutions, funders and journals will require iterative evaluation and improvement. We discuss the goals of these measures, and how they can be implemented, in the hope that this will facilitate action toward improving the transparency, reproducibility and efficiency of scientific research. Leading voices in the reproducibility landscape call for the adoption of measures to optimize key elements of the scientific process.

Background Our knowledge of the broader impacts of antidepressant withdrawal, beyond physical side effects, is limited. Further research is needed to investigate the lived experiences of withdrawal, to aid clinicians on how to guide patients through the process. Aim To explore antidepressant users’ experiences and views on the withdrawal process and how it affected their quality of life across multiple life domains. Design and Setting We conducted in‐depth qualitative interviews with 20 individuals from the community who had attempted to withdraw from Serotonin Reuptake Inhibitor antidepressants in the past year. Method Semi‐structured interviews were conducted online. A topic guide was used to ensure consistency across interviews. The interviews were audio‐recorded and transcribed verbatim and analysed using inductive reflexive thematic analysis. Results Five themes were generated. The first highlighted the challenges of managing the release from emotional blunting and cognitive suppression following antidepressant discontinuation. The second related to the negative impact of withdrawal on close relationships and social interactions. The third showed that concurrent with negative physical symptoms, there was a positive impact on health (exercise was reported by some as a coping mechanism). The fourth theme focused on support from GPs and families, emphasising the importance of mental health literacy in others. The final theme underscored the importance of gradual and flexible tapering in enabling a manageable withdrawal experience, and the consideration of timing. Conclusion The lived experience of withdrawal significantly impacts individuals’ well‐being. Participants emphasised that withdrawal is not just about physical side effects but also affects their emotional, cognitive, and social functioning. Patient and Public Involvement (PPI) Eight people attended individual online meetings to share their experiences of antidepressant withdrawal to help inform the study design and recruitment strategy. Insights from these meetings informed the development of the topic guide. Questions about GP involvement, family relationships, and mood and thinking changes were included based on this PPI work. This ensured the inclusion of topics important to antidepressant users and facilitated the researcher's questioning during the interviews.

Psychotic Experiences (PEs) during adolescence index increased risk for psychotic disorders and schizophrenia in adult life. Working memory (WM) deficits are a core feature of these disorders. Our objective was to examine the relationship between PEs and WM in a general population sample of young people in a case control study. 4744 individuals of age 17-18 from Bristol and surrounding areas (UK) were analyzed in a cross-sectional study nested within the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort study. The dependent variable was PEs, assessed using the semi-structured Psychosis-Like Symptom Interview (PLIKSi). The independent variable was performance on a computerized numerical n-back working memory task. Signal-Detection Theory indices, including standardized hits rate, false alarms rate, discriminability index (d') and response bias (c) from 2-Back and 3-Back tasks were calculated. 3576 and 3527 individuals had complete data for 2-Back and 3-Back respectively. Suspected/definite PEs prevalence was 7.9% (N = 374). Strongest evidence of association was seen between PEs and false alarms on the 2-Back, (odds ratio (OR) = 1.17 [95% confidence intervals (CI) 1.01, 1.35]) and 3-back (OR = 1.35 [1.18, 1.54]) and with c (OR = 1.59 [1.09, 2.34]), and lower d' (OR = 0.76 [0.65, 0.89]), on the 3-Back. Adjustment for several potential confounders, including general IQ, drug exposure and different psycho-social factors, and subsequent multiple imputation of missing data did not materially alter the results. WM is impaired in young people with PEs in the general population. False alarms, rather than poor accuracy, are more closely related to PEs. Such impairment is consistent with different neuropsychological models of psychosis focusing on signal-to-noise discrimination, probabilistic reasoning and impaired reality monitoring as a basis of psychotic symptoms.

Robust conclusions require rigorous statistics. In 2009 a seminal paper described the dangers and prevalence of double-dipping in neuroscience. Ten years on, I consider progress toward statistical rigor in neuroimaging.

Various effective psychotherapies exist for the treatment of depression; however, only approximately half of patients recover after treatment. In efforts to improve clinical outcomes, research has focused on personalised psychotherapy - an attempt to match patients to treatments they are most likely to respond to. The present research aimed to evaluate the benefit of a data-driven model to support clinical decision-making in differential treatment allocation to cognitive-behavioural therapy versus counselling for depression. The present analysis used electronic healthcare records from primary care psychological therapy services for patients receiving cognitive-behavioural therapy ( = 14 544) and counselling for depression ( = 4725). A linear regression with baseline sociodemographic and clinical characteristics was used to differentially predict post-treatment Patient Health Questionnaire (PHQ-9) scores between the two treatments. The benefit of differential prescription was evaluated in a held-out validation sample. On average, patients who received their model-indicated optimal treatment saw a greater improvement (by 1.78 PHQ-9 points). This translated into 4-10% more patients achieving clinically meaningful changes. However, for individual patients, the estimated differences in benefits of treatments were small and rarely met the threshold for minimal clinically important differences. Precision prescription of psychotherapy based on sociodemographic and clinical characteristics is unlikely to produce large benefits for individual patients. However, the benefits may be meaningful from an aggregate public health perspective when applied at scale.

Fear of negative evaluation (FNE) defines social anxiety yet the process of inferring social evaluation, and its potential role in maintaining social anxiety, is poorly understood. We developed an instrumental learning task to model social evaluation learning, predicting that FNE would specifically bias learning about the self but not others. During six test blocks (3 self-referential, 3 other-referential), participants (n = 100) met six personas and selected a word from a positive/negative pair to finish their social evaluation sentences \"I think [you are / George is]…\". Feedback contingencies corresponded to 3 rules, liked, neutral and disliked, with P[positive word correct] = 0.8, 0.5 and 0.2, respectively. As FNE increased participants selected fewer positive words (β = -0.4, 95% CI -0.7, -0.2, p = 0.001), which was strongest in the self-referential condition (FNE × condition 0.28, 95% CI 0.01, 0.54, p = 0.04), and the neutral and dislike rules (FNE × condition × rule, p = 0.07). At low FNE the proportion of positive words selected for self-neutral and self-disliked greatly exceeded the feedback contingency, indicating poor learning, which improved as FNE increased. FNE is associated with differences in processing social-evaluative information specifically about the self. At low FNE this manifests as insensitivity to learning negative self-referential evaluation. High FNE individuals are equally sensitive to learning positive or negative evaluation, which although objectively more accurate, may have detrimental effects on mental health.

Food response inhibition training (food-RIT) is found to aid weight loss and reduce snacking of foods high in sugar, salt and fat. However, these interventions suffer from a lack of adherence, with gamification proposed as a solution to increase engagement. The effect of gamification is unclear, however, with a lack of research investigating the effects of single game elements in improving adherence to interventions. This study investigates whether isolated game elements (social or feedback) improve adherence, engagement and effectiveness of food-RIT compared to a standard non-gamified intervention. Two hundred and fifty-two participants (169 female) were randomly assigned to either non-gamified F-RIT, a training gamified with feedback elements or a training gamified with social elements. Participants completed measures of snacking frequency and food evaluation before and after a 14-day training period, with adherence and motivation recorded during this time. There were no significant effects of adding either feedback or social gamification elements on training adherence, motivation or effectiveness. There was no meaningful support for adding isolated game elements to food-RIT to improve intervention adherence, raising questions about the magnitude of simple gamification effects. Future research may benefit from systematically assessing the combined effects of multiple gamification elements.